Companies and other entities often rely on opinions and feedback from customers, employees, or other individuals. A common method of acquiring feedback is through electronic surveys, including electronic customer ratings and reviews (e.g., ratings and reviews for products, services, businesses, etc.). For example, companies often administer electronic surveys to customers to collect meaningful information about the experience of any number of customers with a particular company or product. With the increased convenience and administration of electronic surveys, companies can collect massive amounts of information and data from millions of customers.
Many conventional systems attempt to conserve processing resources by collecting survey responses having an analysis-friendly format. For example, many electronic surveys include questions that solicit rankings, ranges of numbers, defined categories, binary characterizations, or other types of data that facilitate a less robust analysis of the survey results. However, these types of electronic survey questions fail to gather valuable information from respondents as the type of information that respondents provide is predefined. Indeed, if respondents have an issue that is not explicitly identified as a choice within a predefined choice, then that issue is almost impossible to identify from an electronic survey.
Accordingly, most electronic survey administrators place a high value on collecting free-form text responses from respondents that allow a respondent to provide information within the voice of the respondent. As a result of the massive collection of text responses, however, conventional systems for analyzing the text-responses become computationally bogged down and are computer resource intensive (e.g., processor and memory resources) when attempting to identify specific types of information or sift through millions of text responses to survey questions to analyze trends of information within the responses. Indeed, conventional electronic survey systems often cannot provide robust analysis for free-form text responses, or alternatively, consume large amounts of computing resources to perform an analysis, which takes significant computing time. Due to these limitations, conventional systems do not provide tools for determining overall trends or extracting meaningful information from the massive number of free-form text responses.